Settings

Metadata

  • Tumor sample name: SRR3670028
  • Normal Sample name: SRR3670027
  • Size of MAF data: 1668.05 kb

 

Report configuration

 

1    Potential artifacts filtration

a.    Sequencing quality thresholds
  • tumor depth (tumorDP): 20
  • tumor allele depth (tumorAD): 10
  • normal depth (normalDP): 10
  • VAF: 0.05
  • VAF ratio (tumorVAF/normalVAF): 5

 

b.    Strand of Bias

Strand bias occurs when the genotype inferred from information presented by the forward strand and the reverse strand disagrees. A study showed that post-analysis procedures can cause strand bias, which introduce more SNPs with higher strand bias, which in turn results in more false-positive SNPs 1. Therefore, it is necessary to detect and minimize strand bias in our data.

At present, there are four methods of strand bias detection that are widely used. In a mitochondria heteroplasmy study 2, the calculation of SB was put forward. GATK calculates a strand bias score for each SNP identified, and Samtools also computes a strand bias score based on Fisher’s exact test. Additionally, GATK introduced an updated form of the Fisher Strand Test, StrandOddsRatioSOR annotation, which is believed to be better at taking into account large amounts of data in high coverage situations.

In CaMutQC, either Fisher Strand Test or SOR algorithm can be used to evaluate strand bias and filter variants based on the results. By default, strand bias is detected through SOR algorithm.

  • Method used to detect strand bias: SOR
  • Strand Bias score cutoff: 3

 

c.    Adjacent indel tag

The Adjacent Indel tag is used when a somatic variant was possibly caused by misalignment around a germline or somatic insertion/deletion(indel). By default, CaMutQC will filter any SNV which was within 10bp of an indel found in the tumor sample. Also, the maximum length of an indel is set as 50bp.

Maximum length of an indel: 50

Minimum interval between an indel and an SNV: 10

 

d.    FILTER tag

Some variant callers add a tag if a variant pass the post-filtration after calling. In CaMutQC, users can set a standard tag found in the FILTER column of VCF file to keep variants. PASS will be used in CaMutQC by default.

FILTER tag: PASS

 

2    Candidate variant selection

a.    Database filtration

Some database published germline variants and recurrent artifacts in distinct races. In CaMutQC, based on the parameters we collected 3 4 5, potential germline variants will be removed using annotation from these database(if available) unless CliVar/OMIM/HGMD flags it as pathogenic.

COSMIC, the Catalogue Of Somatic Mutations In Cancer, is the world’s largest and most comprehensive resource for exploring the impact of somatic mutations in human cancer. They have assembled a list of genes that are somatically mutated and causally implicated in human cancer 6, which is called the The Cancer Gene Census and is updated periodically with new genes. In VCF files annotated by VEP, a Existing_variation column normally indicates a gene is on this COSMIC list if it has a annotation ID starts with COSV, COSM or COSN.

In CaMutQC, by default, we use VAF cutoff of 0.01 to remove potential germline variants unless CliVar/OMIM/HGMD flags it as pathogenic.

  • Database included: ExAC, Genomesprojects1000, ESP6500, gnomAD

  • VAF cutoff: 0.01

  • Only keep variants in COSMIC: TRUE

 

b.    Normal depth threshold

To avoid miscalling germline variants and to ensure the quality of variants 4, filtration for normal depth is also applied in CaMutQC as follows.

  • dbsnp Variants: 19
  • Non-dbsnp variants: 8

 

c.    Panel of normals

A Panel of normal or PON is a type of resource used in somatic variant analysis. Basically, if a variant is found in a panel of normals, or is found in more than two normal samples, it is unlikely to be a driven variant during cancer development. PON filtration has been widely used in many researches and projects to discard non-driven variants 3 7 8.

A PON can be generated by users through sequencing a number of normal samples that are as technically similar as possible to the tumor (same exome or genome preparation methods, sequencing technology and so on). Or, a PON can be directly obtained from GATK, which is viewed as one of the most effective filters of false-positive, contamination, and germline variants filter 4.

In CaMutQC, PON filtration dependents on GATK PON datasets, and variant data refers to different versions of genome uses different public GATK panels of normals datasets for filtration.

  • GRCh37: somatic-b37_Mutect2-exome-panel.vcf
  • GRCh38: somatic-hg38_1000g_pon.hg38.vcf

NCBI build version of this dataset: GRCh37

 

d.    Types of variants

Most studies relate to cancer somatic mutations remove certain types of variants in order to better target driven variants, among which exonic and nonsynonymous are two of the most widely used categories for filtration 4 9 10.

In CaMutQC, two categories can be chosen during this filtration step. exonic is the default option, and the other option is nonsynonymous, it will leave you non-synonymous variants. More details could be found at Ensembl Variation and Variant Classification Description.

  • Variant classifications viewed as exonic: RNA, Intron, IGR, 5\'Flank, 3\'Flank, 5\'UTR, 3\'UTR

  • Variant classifications viewed as nonsynonymous: 3'UTR, 5\'UTR, 3\'Flank, Targeted_Region, Silent, Intron, RNA, IGR, Splice_Region, 5\'Flank, lincRNA,De_novo_Start_InFrame, De_novo_Start_OutOfFrame, Start_Codon_Ins, Start_Codon_SNP, Stop_Codon_Del

Type chose for this filtration: exonic

 

e.    Region selection

In this selection, users are able to further select variants related to cancer development by providing a bed file. Variants will be searched only in target regions.

Bed file provided: FALSE

 

3    Filters and flags

Filter Flag Filter Flag
mutFilterQual Q mutFilterPON P
mutFilterSB S mutFilterType T
mutFilterAdj A mutFilterReg R
mutFilterDB D FILTER F
mutFilterNormalDP N

 

4    Target cancer type

Parameters in filtration ans selection process refer to : NULL

Potential artifacts filtration

Statistics

Item Before filtration After filtration
# Variants 773 413
# Genes 698 384
Type of variants Before filtration After filtration
SNP 726 408
DNP 0 0
TNP 0 0
ONP 0 0
INS 18 1
DEL 29 4

 

Visualization

 

1    Flag barplot

2    Type of variants

 

 

3    Distribution of variants in genome

 

 

4    VAF Distribution

 

 

Candidate variant selection

Statistics

Item Before selection After selection
# Variants 413 17
# Genes 384 17
Type of variants Before selection After selection
SNP 408 17
DNP 0 0
TNP 0 0
ONP 0 0
INS 1 0
DEL 4 0

 

Visualization

 

1    Flag barplot

2    Type of variants

 

 

3    Distribution of variants in genome

 

 

4    VAF Distribution

 

 

TMB

Tumor Mutational Burden (TMB) refers to the number of somatic non-synonymous mutations per megabase pair (Mb) in a specific genomic region. In 2015, tumor non-synonymous mutation burden was first confirmed to be related to PD1/PD-L1 cancer immunotherapy 11. Through the analysis of mutation burden of patients with non-small cell lung cancer, the clinical response and survival rate and other indicators, researcherst confirmed that the higher the TMB of cancer patients have, the better the effect of tumor immunotherapy would get. This conclusion was subsequently verified in other cancer types, such as malignant melanoma 12 and small cell lung cancer 13. Therefore, TMB has become one of the predictive biomarkers of immune checkpoint and inhibitor immunotherapy in cancer treatment 14.

There are many calculation methods for TMB, including WGS, WES, regional sequencing using gene panels, and sequencing of circulating tumor DNA in tumor samples or blood 15. Different from scientific research, the conventional method of determining TMB in clinical practice is to target-sequence tumor samples, which is to hybridize and capture the exon and intron regions of a certain number of cancer-related genes, without the need for WES sequencing. Currently, the most widely used panels are FoundationOneCDx (F1CDx) and MSK-IMPACT 9. The former only needs to sequence tumor samples, while the latter requires both the tumor sample and its matched normal sample to be sequenced. Both of them have certification from US Food and Drug Administration (FDA).

In CaMutQC, three methods are supported for TMB calculation, including FoundationOne, MSK-IMPACT (3 versions of genelist) and Pan-cancer panel 16. By default, TMB is calculated using MSK-IMPACT method (gene panel version 3, 468 genes).

Size of the targeted genomic region: Calculation not asked.

Estimated tumor mutational burden (TMB): Calculation not asked.

 

Specific Inspection

 

SessionInfo

sessionInfo()
## R version 4.0.4 (2021-02-15)
## Platform: x86_64-w64-mingw32/x64 (64-bit)
## Running under: Windows 10 x64 (build 19042)
## 
## Matrix products: default
## 
## locale:
## [1] LC_COLLATE=Chinese (Simplified)_China.936 
## [2] LC_CTYPE=Chinese (Simplified)_China.936   
## [3] LC_MONETARY=Chinese (Simplified)_China.936
## [4] LC_NUMERIC=C                              
## [5] LC_TIME=Chinese (Simplified)_China.936    
## 
## attached base packages:
## [1] stats     graphics  grDevices utils     datasets  methods   base     
## 
## other attached packages:
## [1] DT_0.17       ggplot2_3.3.3 vcfR_1.12.0   stringr_1.4.0 dplyr_1.0.5  
## 
## loaded via a namespace (and not attached):
##  [1] tinytex_0.30      tidyselect_1.1.0  xfun_0.22         bslib_0.2.4      
##  [5] memuse_4.1-0      purrr_0.3.4       splines_4.0.4     lattice_0.20-41  
##  [9] testthat_3.0.2    colorspace_2.0-0  vctrs_0.3.6       generics_0.1.0   
## [13] htmltools_0.5.1.1 viridisLite_0.3.0 yaml_2.2.1        mgcv_1.8-34      
## [17] utf8_1.2.1        rlang_0.4.10      pillar_1.5.1      jquerylib_0.1.3  
## [21] glue_1.4.2        withr_2.4.1       DBI_1.1.1         lifecycle_1.0.0  
## [25] munsell_0.5.0     gtable_0.3.0      htmlwidgets_1.5.3 evaluate_0.14    
## [29] labeling_0.4.2    knitr_1.31        permute_0.9-5     crosstalk_1.1.1  
## [33] parallel_4.0.4    fansi_0.4.2       highr_0.8         Rcpp_1.0.6       
## [37] pinfsc50_1.2.0    scales_1.1.1      desc_1.3.0        pkgload_1.2.0    
## [41] vegan_2.5-7       jsonlite_1.7.2    farver_2.1.0      digest_0.6.27    
## [45] stringi_1.5.3     rprojroot_2.0.2   grid_4.0.4        tools_4.0.4      
## [49] magrittr_2.0.1    sass_0.3.1        tibble_3.1.0      cluster_2.1.1    
## [53] crayon_1.4.1      ape_5.4-1         pkgconfig_2.0.3   MASS_7.3-53.1    
## [57] ellipsis_0.3.1    Matrix_1.3-2      assertthat_0.2.1  rmarkdown_2.7    
## [61] R6_2.5.0          nlme_3.1-152      compiler_4.0.4

 

Reference

 

  1. Guo Y, Li J, Li CI, Long J, Samuels DC, Shyr Y. The effect of strand bias in Illumina short-read sequencing data. BMC Genomics. 2012;13:666. Published 2012 Nov 24. doi:10.1186/1471-2164-13-666

  2. Guo Y, Cai Q, Samuels DC, et al. The use of next generation sequencing technology to study the effect of radiation therapy on mitochondrial DNA mutation. Mutat Res. 2012;744(2):154-160. doi:10.1016/j.mrgentox.2012.02.006

  3. Pereira B, Chin SF, Rueda OM, et al. The somatic mutation profiles of 2,433 breast cancers refines their genomic and transcriptomic landscapes. Nat Commun. 2016;7:11479. Published 2016 May 10. doi:10.1038/ncomms11479

  4. Ellrott K, Bailey MH, Saksena G, et al. Scalable Open Science Approach for Mutation Calling of Tumor Exomes Using Multiple Genomic Pipelines. Cell Syst. 2018;6(3):271-281.e7. doi:10.1016/j.cels.2018.03.002

  5. Xue R, Chen L, Zhang C, et al. Genomic and Transcriptomic Profiling of Combined Hepatocellular and Intrahepatic Cholangiocarcinoma Reveals Distinct Molecular Subtypes. Cancer Cell. 2019;35(6):932-947.e8. doi:10.1016/j.ccell.2019.04.007

  6. Futreal PA, Coin L, Marshall M, et al. A census of human cancer genes. Nat Rev Cancer. 2004;4(3):177-183. doi:10.1038/nrc1299

  7. Brastianos PK, Carter SL, Santagata S, et al. Genomic Characterization of Brain Metastases Reveals Branched Evolution and Potential Therapeutic Targets. Cancer Discov. 2015;5(11):1164-1177. doi:10.1158/2159-8290.CD-15-0369

  8. Sethi NS, Kikuchi O, Duronio GN, et al. Early TP53 alterations engage environmental exposures to promote gastric premalignancy in an integrative mouse model. Nat Genet. 2020;52(2):219-230. doi:10.1038/s41588-019-0574-9

  9. Cheng DT, Mitchell TN, Zehir A, et al. Memorial Sloan Kettering-Integrated Mutation Profiling of Actionable Cancer Targets (MSK-IMPACT): A Hybridization Capture-Based Next-Generation Sequencing Clinical Assay for Solid Tumor Molecular Oncology. J Mol Diagn. 2015;17(3):251-264. doi:10.1016/j.jmoldx.2014.12.006

  10. Sakamoto H, Attiyeh MA, Gerold JM, et al. The Evolutionary Origins of Recurrent Pancreatic Cancer. Cancer Discov. 2020;10(6):792-805. doi:10.1158/2159-8290.CD-19-1508

  11. Rizvi NA, Hellmann MD, Snyder A, et al. Cancer immunology. Mutational landscape determines sensitivity to PD-1 blockade in non-small cell lung cancer. Science. 2015;348(6230):124-128. doi:10.1126/science.aaa1348

  12. Snyder A, Makarov V, Merghoub T, et al. Genetic basis for clinical response to CTLA-4 blockade in melanoma [published correction appears in N Engl J Med. 2018 Nov 29;379(22):2185]. N Engl J Med. 2014;371(23):2189-2199. doi:10.1056/NEJMoa1406498

  13. Hellmann MD, Callahan MK, Awad MM, et al. Tumor Mutational Burden and Efficacy of Nivolumab Monotherapy and in Combination with Ipilimumab in Small-Cell Lung Cancer [published correction appears in Cancer Cell. 2019 Feb 11;35(2):329]. Cancer Cell. 2018;33(5):853-861.e4. doi:10.1016/j.ccell.2018.04.001

  14. Lee M, Samstein RM, Valero C, Chan TA, Morris LGT. Tumor mutational burden as a predictive biomarker for checkpoint inhibitor immunotherapy. Hum Vaccin Immunother. 2020;16(1):112-115. doi:10.1080/21645515.2019.1631136

  15. Stenzinger A, Allen JD, Maas J, et al. Tumor mutational burden standardization initiatives: Recommendations for consistent tumor mutational burden assessment in clinical samples to guide immunotherapy treatment decisions. Genes Chromosomes Cancer. 2019;58(8):578-588. doi:10.1002/gcc.22733

  16. Xu Z, Dai J, Wang D, et al. Assessment of tumor mutation burden calculation from gene panel sequencing data. Onco Targets Ther. 2019;12:3401-3409. Published 2019 May 6. doi:10.2147/OTT.S196638